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Solar Variability Assessment in the Built Environment: Model Development and Application to Grid Integration
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Solid State Physics. (Built Environment Energy Systems Group)ORCID iD: 0000-0001-6586-4932
2017 (English)Doctoral thesis, comprehensive summary (Other academic)Alternative title
Variationer i Solelgenerering i den Byggda Miljön : Modellutveckling och Integration i Elnätet (Swedish)
Abstract [en]

During the 21st century there has been a rapid increase in grid-connected photovoltaic (PV) capacity globally, due to falling system component prices and introduction of various economic incentives. To a large extent, PV systems are installed on buildings, which means they are widely distributed and located close to the power consumer, in contrast to conventional power plants. The intermittency of solar irradiance poses challenges to the integration of PV, which may be mitigated if properly assessing the solar resource. In this thesis, methods have been developed for solar variability and resource assessment in the built environment on both national and local level, and have been applied to grid integration studies. On national level, a method based on building statistics was developed that reproduces the hourly PV power generation in Sweden with high accuracy; correlation between simulated and real power generation for 2012 and 2013 were 0.97 and 0.99, respectively. The model was applied in scenarios of high penetration of intermittent renewable energy (IRE) in the Nordic synchronous power system, in combination with similar models for wind, wave and tidal power. A mix of the IRE resources was sought to minimise the variability in net load (i.e., load minus IRE, nuclear and thermal power). The study showed that a fully renewable Nordic power system is possible if hydropower operation is properly planned for. However, the contribution from PV power would only be 2-3% of the total power demand, due to strong diurnal and seasonal variability. On local level, a model-driven solar resource assessment method was developed based on low-resolution LiDAR (Light Detection and Ranging) data. It was shown to improve the representation of buildings, i.e., roof shape, tilt and azimuth, over raster-based methods, i.e., digital surface models (DSM), which use the same LiDAR data. Furthermore, the new method can provide time-resolved data in contrast to traditional solar maps, and can thus be used as a powerful tool when studying the integration of high penetrations of PV in the distribution grid. In conclusion, the developed methods fill important gaps in our ability to plan for a fully renewable power system.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2017. , p. 92
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1598
Keywords [en]
Solar Variability, Photovoltaics, Grid Integration, Distributed Generation, LiDAR, GIS
National Category
Energy Systems
Research subject
Engineering Science
Identifiers
URN: urn:nbn:se:uu:diva-332714ISBN: 978-91-513-0149-5 (print)OAI: oai:DiVA.org:uu-332714DiVA, id: diva2:1154045
Public defence
2017-12-21, Häggsalen, Lägerhyddsvägen 1, Uppsala, 13:15 (English)
Opponent
Supervisors
Available from: 2017-11-29 Created: 2017-11-01 Last updated: 2018-03-07
List of papers
1. Variability Assessment and Forecasting of Renewables: A Review for Solar, Wind, Wave and Tidal Resources
Open this publication in new window or tab >>Variability Assessment and Forecasting of Renewables: A Review for Solar, Wind, Wave and Tidal Resources
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2015 (English)In: Renewable & sustainable energy reviews, ISSN 1364-0321, E-ISSN 1879-0690, Vol. 44, p. 356-375Article in journal (Refereed) Published
National Category
Energy Engineering Engineering and Technology
Research subject
Engineering Science with specialization in Science of Electricity; Engineering Science with specialization in Solid State Physics
Identifiers
urn:nbn:se:uu:diva-225870 (URN)10.1016/j.rser.2014.12.019 (DOI)000351324300025 ()
Available from: 2014-06-09 Created: 2014-06-09 Last updated: 2018-08-01
2. Development and validation of a wide-area model of hourly aggregate solar power generation
Open this publication in new window or tab >>Development and validation of a wide-area model of hourly aggregate solar power generation
2016 (English)In: Energy, ISSN 0360-5442, E-ISSN 1873-6785, Vol. 102, p. 559-566Article in journal (Refereed) Published
Abstract [en]

The impact of photovoltaics (PV) on the power system becomes increasingly important to study as the penetration of PV has increased rapidly over the last decade. A physical model for aggregated PV generation has been developed for the Swedish spot market areas. Information about PV systems within the Swedish electricity certificate system and irradiance data from the meteorological model STRÅNG were used as inputs. The model was trained and validated against production data reported to the Swedish transmission system operator. Our model shows high correlation (0.95-0.99) to reported historical production data. However, it overestimates extreme 1h ramp rates, which are -20% and 22% for down- and up-ramps respectively, compared to -13% and 14% for the reported data. Furthermore a weighting function was developed, which takes demography, available solar irradiance and today's PV deployment into account, to model likely deployment in a Swedish high penetration scenario, where PV covers 6% of the total annual power demand. The difference in extreme 1 and 4 hour step changes before and after introducing PV is small. The model could thus be used with confidence to model the impact on the power system for future scenarios of high PV penetration.

Keywords
PV power; Step changes; Solar variability; Physical model
National Category
Energy Systems
Identifiers
urn:nbn:se:uu:diva-265450 (URN)10.1016/j.energy.2016.02.085 (DOI)000375889400050 ()
Funder
StandUp
Available from: 2015-10-29 Created: 2015-10-29 Last updated: 2018-02-20Bibliographically approved
3. Net load variability in Nordic countries with a highly or fully renewable power system
Open this publication in new window or tab >>Net load variability in Nordic countries with a highly or fully renewable power system
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2016 (English)In: Nature Energy, ISSN 2058-7546, Vol. 1, p. 1-8, article id 16175Article in journal (Refereed) Published
Abstract [en]

Increasing the share of intermittent renewable energy (IRE) resources such as solar, wind, wave and tidal energy in a power system poses a challenge in terms of increased net load variability. Fully renewable power systems have previously been analysed, but more systematic analyses are needed that explore the effect of different IRE mixes on system-wide variability across different timescales and the optimal combinations of IRE for reducing variability on a given timescale. Here we investigate these questions for the Nordic power system. We show that the optimal mix of IRE is dependent on the frequency band considered. Long-term (>4 months) and short-term (<2 days) fluctuations can be similar to today’s, even for a fully renewable system. However, fluctuations with periods in between will inevitably increase significantly. This study indicates that, from a variability point of view, a fossil- and nuclear-free Nordic power system is feasible if properly balanced by hydropower.

National Category
Materials Engineering
Identifiers
urn:nbn:se:uu:diva-302836 (URN)10.1038/NENERGY.2016.175 (DOI)000394793000001 ()
Funder
StandUpStandUp for Wind
Available from: 2016-09-11 Created: 2016-09-11 Last updated: 2018-02-20
4. Comparing the capability of low- and high-resolution LiDAR data with application to solar resource assessment, roof type classification and shading analysis
Open this publication in new window or tab >>Comparing the capability of low- and high-resolution LiDAR data with application to solar resource assessment, roof type classification and shading analysis
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2017 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 205, p. 1216-1230Article in journal (Refereed) Published
Abstract [en]

LiDAR (Light Detection and Ranging) data have recently gained popularity for use in solar resource assessment and solar photovoltaics (PV) suitability studies in the built environment due to robustness at identifying building orientation, roof tilt and shading. There is a disparity in the geographic coverage of low- and high-resolution LiDAR data (LL and LH, respectively) between rural and urban locations, as the cost of the latter is often not justified for rural areas where high PV penetrations often pose the greatest impact on the electricity distribution network. There is a need for a comparison of the different resolutions to assess capability of LL. In this study, we evaluated and improved upon a previously reported methodology that derives roof types from a LiDAR-derived, low-resolution Digital Surface Model (DSM) with a co-classing routine. Key improvements to the methodology include: co-classing routine adapted for raw LiDAR data, applicability to differing building type distribution in study area, building height and symmetry considerations, a vector-based shading analysis of building surfaces and the addition of solar resource assessment capability.

Based on the performance of different LiDAR resolutions within the developed model, a comparison between LL (0.5-1 pts/m(2)) and LH (6-8 pts/m(2)) LiDAR data was applied; LH can confidently be used to evaluate the applicability of LL, due to its significantly higher point density and therefore accuracy. We find that the co-classing methodology works satisfactory for LL for all types of building distributions. Roof-type identification errors from incorrect co-classing were rare (< 1%) with LL. Co-classing buildings using LL improves accuracy of roof-type identification in areas with homogeneous distribution of buildings, here from 78% to 86% in accuracy. Contrastingly, co-classing accuracy using LH is marginally reduced for all building distributions from 94.8% to 94.4%. We adapt the Hay and Davies solar transposition model to include shading. The shading analysis demonstrates similarity of results between LL and LH. We find that the proposed methodology can confidently be used for solar resource assessments on buildings when only LiDAR data of low-resolution (< 1 pts/m(2)) is available.

Place, publisher, year, edition, pages
Elsevier, 2017
Keywords
LiDAR, Solar resource assessment, Shading, Building classification, Low-resolution, High-resolution
National Category
Energy Systems
Research subject
Engineering Science
Identifiers
urn:nbn:se:uu:diva-332226 (URN)10.1016/j.apenergy.2017.08.045 (DOI)000414817100098 ()
Available from: 2017-10-25 Created: 2017-10-25 Last updated: 2018-02-20Bibliographically approved
5. Identification of PV system shading using a LiDAR-based solar resource assessment model: an evaluation and cross-validation
Open this publication in new window or tab >>Identification of PV system shading using a LiDAR-based solar resource assessment model: an evaluation and cross-validation
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2018 (English)In: Solar Energy, ISSN 0038-092X, E-ISSN 1471-1257, Vol. 159, p. 157-172Article in journal (Refereed) Published
Abstract [en]

Photovoltaic (PV) systems are subject to several different systematic de-rating factors, such as soiling, degradation, inverter mismatch and shading. With increasing penetration of PV in the local grid, Distribution Network Service Providers (DNSPs) are inclined to assess such losses, in order to accurately estimate the total regional power output of distributed PV. The most influential de-rating factor is shading, which can cause ramps on the generated power output, similar to clouds. In this study we evaluate and compare two fundamentally different methods for module orientation parametrisation and shading analysis of PV systems that have been developed in previous work. In the first method, LiDAR (Light Detection and Ranging) data are used to derive the PV module orientation and shading, referred to herein as LiDAR model. The second method, referred to as the QCPV-Tuning model, is based on reported PV power generation, which is firstly quality controlled and parametrised in order to derive module orientation and a loss factor, LF, representing systematic de-rating factors. Secondly, variations in de-ratings throughout the day, mainly due to shading, are explored in a process referred to as Tuning. For both methods, binary time series are derived expressing the presence of shading, which are used to evaluate how the methods corroborate. We evaluate four cases; case 1) evaluates the original versions of the LiDAR and QCPV-Tuning models, while in case 2-4 improvements to the models are introduced. A new filter for extracting representative LiDAR data points for the shading analysis was introduced for the LiDAR model (case 2). For the QCPV-Tuning model significant inaccuracies in the parametrisation of the module orientation were identified due to strong shading in either morning or evening and were thus corrected to observed parameters (case 3). For case 4) improvements on both models were introduced. The Pearson correlation coefficients of shading events for the methods were 0.28, 0.36, 0.42 and 0.50 for case 1-4, respectively. A mismatch in the timing of shading events motivated the comparison of the mean hourly shading, with correlation coefficients of 0.34, 0.43, 0.49 and 0.57 for case 1-4, respectively. The results of this study show that both methods can confidently be used for solar resource assessment, given the suggested improvements.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
Solar resource assessment, Shading, PV Tuning, LiDAR
National Category
Energy Systems
Research subject
Engineering Science
Identifiers
urn:nbn:se:uu:diva-332235 (URN)10.1016/j.solener.2017.10.061 (DOI)000423007300016 ()
Available from: 2017-10-25 Created: 2017-10-25 Last updated: 2018-03-12Bibliographically approved
6. Self-consumption enhancement and peak shaving of residential photovoltaics using storage and curtailment
Open this publication in new window or tab >>Self-consumption enhancement and peak shaving of residential photovoltaics using storage and curtailment
2016 (English)In: Energy, ISSN 0360-5442, E-ISSN 1873-6785, Vol. 112, p. 221-231Article in journal (Refereed) Published
Abstract [en]

Increasing the self-consumption of photovoltaic (PV) power is an important aspect to integrate more PV power in the power system. The profit for the PV system owner can increase and the stress on the power grid can be reduced. Previous research in the field has focused on either self-consumption of PV power in individual buildings or PV power curtailment for voltage control. In this paper self-consumption of residential PV power in a community of several single-family houses was investigated using high-resolution irradiance and power consumption data. Cases with individual or shared battery energy storages for the houses were examined. PV power curtailment was investigated as a method to reduce feed-in power to the grid, i.e. peak shaving. Results indicated that the self-consumption ratio increased when using shared instead of individual storage. Reducing the feed-in power from the community by almost 50% only led to maximum 7% yearly production losses due to curtailment and storage losses. The economics for shared storage are slightly better than for individual ones. These results suggest that residential PV-battery systems should use (i) shared energy storage options if local regulations allow it and (ii) PV power curtailment if there are incentives to lower the feed-in power.

Place, publisher, year, edition, pages
Elsevier, 2016
Keywords
Photovoltaics, Solar energy, Self-consumption, Energy storage, Battery, Curtailment
National Category
Energy Engineering
Research subject
Engineering Science with specialization in Solid State Physics
Identifiers
urn:nbn:se:uu:diva-283612 (URN)10.1016/j.energy.2016.06.039 (DOI)000385318700021 ()
Projects
Småskalig solel i byggnader – kraft för förändring i energisystem och vardaglivet
Funder
Swedish Energy Agency, P37511-1
Available from: 2016-04-13 Created: 2016-04-13 Last updated: 2018-10-10Bibliographically approved
7. Large-scale integration of photovoltaic power in a distribution grid using power curtailment and energy storage
Open this publication in new window or tab >>Large-scale integration of photovoltaic power in a distribution grid using power curtailment and energy storage
2017 (English)In: Solar Energy, ISSN 0038-092X, E-ISSN 1471-1257, Vol. 155, p. 1319-1325Article in journal (Refereed) Published
Keywords
Photovoltaics, Power distribution system, Energy storage, Power and voltage control, Overvoltage
National Category
Energy Engineering
Research subject
Engineering Science
Identifiers
urn:nbn:se:uu:diva-328066 (URN)10.1016/j.solener.2017.07.083 (DOI)000414819900057 ()
Projects
Småskalig solel i byggnader – kraft för förändring i energisystem och vardaglivetUtvärdering av tekniska lösningar för att hantera omfattande anslutning av solcellssystem i eldistributionsnät
Funder
Swedish Energy Agency, P37511-1
Note

Photovoltaic (PV) power generation is an important component for the future energy system. High penetrationof PV power in a power distribution system might however lead to problems with overvoltage and overload. In this study, a method for PV power curtailment and placement of decentralized energy storage is developed to control voltage, feeder currents and distribution substation overloading. The method determines an individual feed-in power limit for each PV system owner based on a voltage-power relationship. Measured data from a 10 kV/400 V three-phase distribution grid in the Swedish municipality of Herrljunga with more than 5000 end-users and simulated PV electricity production data are used for a case study to verify the model. The method is evaluated for yearly PV electricity productionof up to 100% of the yearly electricity consumption. The results show that the method is able to prevent overvoltage for all penetration levels in the studied distribution grid, reduce the number of feeders affected by overcurrent and lower the maximum load on the two substations.

Available from: 2017-08-16 Created: 2017-08-16 Last updated: 2018-10-10Bibliographically approved

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